##           used (Mb) gc trigger  (Mb) max used (Mb)
## Ncells 1027279 54.9    2127124 113.7  1296541 69.3
## Vcells 1721196 13.2    8388608  64.0  2221245 17.0

Definitions

Ciudad Trip Collection Questionnaire
Bogota2019 Moving from one part to another with a specific reason/motive, a definite hour of start and end, a mode of transport, and a duration greater than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration Trips made the day of reference, i.e., the day before the survey. …
Mexico Moving from one part to another with a specific reason/motive, using one or multiples modes of transport Trips made during the week (Tuesday, Wednesday, Thursday) and in Saturdays (weekends) …
Cali Moving from one part to another with a specific reason/motive and a duration longer than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration Trips made the day of reference, i.e., the day before the survey. …
Medellin Couldn’t find the definition Trips made the day of reference, i.e., last 24 hours …
Santiago Any movement carried out on public roads with a purpose determined, between two places (origin and destination) at a certain time of day; It can be carried out in several modes of transport and consist of one or more stages Trips made in working days (regular season), in weekends (regular season) and in working days(summer season) …

Summary table

Bogota2015 Bogota2019 Bogota2019_longer15 Mexico Mexico_weekdays Mexico_weekends Medellin Cali Santiago
Min. 0.0 0.0 0.0 0.0 1 1.0 1.0 0.0 0.0
1st Qu. 14.0 15.0 25.0 20.0 15 15.0 15.0 10.0 15.0
Median 32.2 30.0 45.0 43.0 30 30.0 30.0 25.0 30.0
Mean 39.7 50.6 58.6 52.4 43 43.2 33.7 42.2 36.9
3rd Qu. 61.6 60.0 75.0 75.0 60 60.0 45.0 45.0 50.0
Max. 553.7 1110.0 1110.0 1200.0 840 735.0 600.0 1282.0 1335.0
NA’s 22515.0 10319.0 13899.0 0.0 17964 37916.0 6494.0 12618.0 14066.0

Density plot

These plots are interactive so we can zoom in and out, and select cities.

ggplotly(
  ggplot() + 
    geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 , 
                          data = bogota_2015) +
    geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 , 
               data = bogota_2019) +
    geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 , 
               data = bogota_2019_longer15) +
    geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 , 
                 data = mexico) +
    geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 , 
                 data = mexico_weekdays) + 
    geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 , 
                 data = mexico_weekends) +
    geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 , 
                 data = medellin) +
    geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 , 
                 data = cali) +
    geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 , 
                 data = santiago) 
    )
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: Removed 10319 rows containing non-finite values (stat_density).
## Warning: Removed 13899 rows containing non-finite values (stat_density).
## Warning: Removed 17964 rows containing non-finite values (stat_density).
## Warning: Removed 37916 rows containing non-finite values (stat_density).
## Warning: Removed 6494 rows containing non-finite values (stat_density).
## Warning: Removed 12618 rows containing non-finite values (stat_density).
## Warning: Removed 14066 rows containing non-finite values (stat_density).

Density plot by mode

Bogota 2015

ggplotly(ggplot() + 
    geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                 alpha = .3 , data = bogota_2015))
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Bogota 2019

ggplotly(ggplot() + 
    geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                 alpha = .3 , data = bogota_2019))
## Warning: Removed 10319 rows containing non-finite values (stat_density).

Bogota 2019 walking trips longer than 15 minutes

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = bogota_2019_longer15))
## Warning: Removed 13899 rows containing non-finite values (stat_density).

Mexico

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = mexico))

Mexico weekdays

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = mexico_weekdays))
## Warning: Removed 17964 rows containing non-finite values (stat_density).

Mexico weekends

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = mexico_weekends))
## Warning: Removed 37916 rows containing non-finite values (stat_density).

Medellin

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = medellin))
## Warning: Removed 6494 rows containing non-finite values (stat_density).

Cali

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = cali))
## Warning: Removed 12618 rows containing non-finite values (stat_density).

Santiago

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = santiago))
## Warning: Removed 14066 rows containing non-finite values (stat_density).

Comparison of walking trips

ggplotly(
  ggplot() + 
    geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 , 
                 data = bogota_2015 %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 , 
                 data = bogota_2019 %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 , 
                 data = bogota_2019_longer15 %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 , 
                 data = mexico %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 , 
                 data = mexico_weekdays %>% 
                   filter(trip_mode == "walk")) + 
    geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 , 
                 data = mexico_weekends %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 , 
                 data = medellin %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 , 
                 data = cali %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 , 
                 data = santiago %>% 
                   filter(trip_mode == "walk")) 
)
## Warning: Removed 4 rows containing non-finite values (stat_density).
## Warning: Removed 1 rows containing non-finite values (stat_density).
## Warning: Removed 398 rows containing non-finite values (stat_density).
## Warning: Removed 2 rows containing non-finite values (stat_density).

Comparison of cycling trips

ggplotly(
  ggplot() + 
    geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 , 
                 data = bogota_2015 %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 , 
                 data = bogota_2019 %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 , 
                 data = bogota_2019_longer15 %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 , 
                 data = mexico %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 , 
                 data = mexico_weekdays %>% 
                   filter(trip_mode == "bicycle")) + 
    geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 , 
                 data = mexico_weekends %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 , 
                 data = medellin %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 , 
                 data = cali %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 , 
                 data = santiago %>% 
                   filter(trip_mode == "bicycle")) 
)
## Warning: Removed 40 rows containing non-finite values (stat_density).